New publications related to CORE C08

@inproceedings{Gombert:et:al:2026:a,
  title     = {Rubrics as Semantic Subspaces: A Unified Approach to Rubric-based
               Constructed Response Scoring across Short Answers and Essays},
  author    = {Gombert, Sebastian and Hahn, Sonja and Andersen, Nico and Camus, Leon
               and Sun, Zhifan and Nguyen, Ngoc Nhu Hao and Zehner, Fabian and Cong, Longwei
               and Mehler, Alexander and Drachsler, Hendrik},
  editor    = {Kochmar, Ekaterina and Alhafni, Bashar and Bann{\`o}, Stefano
               and Bexte, Marie and Burstein, Jill and Horbach, Andrea and Laarmann-Quante, Ronja
               and Tack, Anais and Yaneva, Victoria and Yuan, Zheng},
  booktitle = {Proceedings of the 21st Workshop on Innovative Use of {NLP} for
               Building Educational Applications ({BEA} 2026)},
  month     = {jul},
  keywords  = {core, core_c08},
  year      = {2026},
  address   = {San Diego, California, USA},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2026.bea-1.44/},
  doi       = {10.18653/v1/2026.bea-1.44},
  pages     = {624--634},
  isbn      = {979-8-89176-409-5},
  pdf       = {https://aclanthology.org/2026.bea-1.44.pdf},
  abstract  = {Rubrics are the primary reference for manual scoring of constructed
               responses, and there is growing interest in their use in automated
               scoring methodologies. In this work, we propose Aspect-Grounded
               Rubric{--}Answer Alignment (AGRAA), a rubric-based end-to-end
               scoring framework that models rubric descriptors as latent aspect
               spaces. Concretely, rubric descriptors are represented as low-dimensional
               subspaces derived from contextualised transformer embeddings,
               and student responses are scored according to how strongly their
               representations align with these rubric-induced spaces relative
               to the residual space outside them. This formulation provides
               a geometrically grounded interpretation of rubric-based scoring
               while enabling end-to-end training with standard transformer encoders.
               We introduce three distinct architectural variants and evaluate
               them on multiple short-answer and essay scoring datasets. Across
               these tasks, AGRAA achieves predictive performance highly competitive
               with strong neural and feature-based baselines. In addition, the
               framework yields interpretable intermediate representations that
               expose which rubric-defined aspects contribute to scoring decisions,
               enabling decision-aligned explanations grounded in rubric descriptors.}
}

@inproceedings{Gombert:et:al:2026:b,
  author    = {Gombert, Sebastian and Romano, Gianluca and Camus, Leon and Baumartz, Daniel
               and Gon{\c{c}}alves Ribeiro, Fabiola and Mehler, Alexander and Drachsler, Hendrik},
  title     = {{NeoBridge}: A Scalable Platform for Assessment Orchestration
               and Log Data Collection in Online Reasoning Assessments},
  booktitle = {Proceedings of the Twenty-first European Conference on Technology
               Enhanced Learning},
  series    = {ECTEL 2026},
  address   = {Valencia, Spain},
  year      = {2026},
  keywords  = {core, core_c08},
  eventdate = {2026-09-14/2026-09-18},
  note      = {accepted}
}